Using a Kernel Adatron for Object Classification with RCS Data
Abstract
Rapid identification of object from radar cross section (RCS) signals is important for many space and military applications. This identification is a problem in pattern recognition which either neural networks or support vector machines should prove to be high-speed. Bayesian networks would also provide value but require significant preprocessing of the signals. In this paper, we describe the use of a support vector machine for object identification from synthesized RCS data. Our best results are from data fusion of X-band and S-band signals, where we obtained 99.4%, 95.3%, 100% and 95.6% correct identification for cylinders, frusta, spheres, and polygons, respectively. We also compare our results with a Bayesian approach and show that the SVM is three orders of magnitude faster, as measured by the number of floating point operations.
Cite
@article{arxiv.1005.5337,
title = {Using a Kernel Adatron for Object Classification with RCS Data},
author = {Marten F. Byl and James T. Demers and Edward A. Rietman},
journal= {arXiv preprint arXiv:1005.5337},
year = {2010}
}
Comments
This material is based upon work supported by US Army Space & Missile Command under Contract Number W9113M-07-C-0204. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily re flect the views of US Army Space & Missile Command